Gemma 4 E4B DECKARD HERETIC NVFP4

gemma

Open WeightModel weights are publicly available — can be downloaded and self-hosted

Released April 2026context N/A

A third-party fine-tune of Google's Gemma 4 E4B base, published by AEON-7 and quantized to NVIDIA FP4 precision. The FP4 quantization makes it well-suited for efficient inference on compatible NVIDIA hardware, trading some numerical precision for reduced memory footprint. Specific behavioral changes introduced by AEON-7's fine-tuning are not well-documented in available sources.

Capabilities

Capability scores are AI-generated based on model documentation, benchmarks, and technical specifications. Learn more

Multilingual

Moderate

Instruction Following

Moderate

Tool Use

Use Case Fit

Fit scores are AI-generated based on model capabilities, intended use, and technical specifications. Learn more

Gemma 4 E4B DECKARD HERETIC NVFP4

gemma

Open WeightModel weights are publicly available — can be downloaded and self-hosted

Released April 2026context N/A

A third-party fine-tune of Google's Gemma 4 E4B base, published by AEON-7 and quantized to NVIDIA FP4 precision. The FP4 quantization makes it well-suited for efficient inference on compatible NVIDIA hardware, trading some numerical precision for reduced memory footprint. Specific behavioral changes introduced by AEON-7's fine-tuning are not well-documented in available sources.

Capabilities

Capability scores are AI-generated based on model documentation, benchmarks, and technical specifications. Learn more

Multilingual

Moderate

Instruction Following

Moderate

Tool Use

Use Case Fit

Fit scores are AI-generated based on model capabilities, intended use, and technical specifications. Learn more

Glossary

FP4 PrecisionA ultra-low precision format using 4-bit floating-point numbers to represent model weights, enabling extreme compression.FP4 QuantizationA compression technique that represents model weights using only 4-bit floating-point numbers instead of larger formats, reducing memory usage and speeding up inference.Fine-TuneA model created by training an existing pre-trained model on new data to specialize it for specific tasks or behaviors.Fine-TuningThe process of further training a pre-trained model on new data to adapt it for specific tasks or domains.InferenceThe process of running a trained model to generate predictions or outputs from new inputs.Memory FootprintThe amount of RAM or storage space a model requires to run, which is critical for deployment on resource-constrained devices.PrecisionThe level of numerical detail a model uses to represent its internal values; higher precision means more accurate calculations but requires more memory.QuantizationReducing a model's numerical precision (e.g., from 16-bit to 4-bit) to shrink memory usage and speed up inference.QuantizedA technique that reduces a model's size and memory usage by storing weights with lower precision (fewer bits), trading some accuracy for efficiency.